import numpy as np
import pymysql 
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from joblib import dump
import requests
import datetime

class weatherUtils(object):

    def __init__(self):
        self.date_list =[
            '2024-07-01',
            '2024-08-01',
            '2024-09-01',
            '2024-10-01',
            '2024-11-01',
            '2024-12-01',
        ]
        self.url='http://v1.yiketianqi.com/api'
    
    
    def get_data(self):
        """
        获取天气数据
        """
        date_list=[]
        for d in self.date_list:
            conf={
                'appid': '81313577',
                'appsecret': 'OvgEz5Xw',
                'version': 'history',
                'year': d[:4],
                'month': d[5:7],
                'city': '南昌',

            }
            res = requests.get(self.url, params=conf)
            res_data=res.json()
            for i in res_data['data']:
                date_list.append({
                    'date': datetime.datetime.strptime(i['ymd'], "%Y-%m-%d"),
                    'bWendu': i['bWendu'],
                    'yWendu': i['yWendu'],
                    'tianqi': i['tianqi'],
                    'fengxiang': i['fengxiang'],
                    'fengli': i['fengli'],
                })
            df = pd.DataFrame(date_list)
            df.to_csv('timeing/weather_data.csv', index=False)
class MySQLUtils(object):
    """MySQL类
    """
    def __init__(self) -> None:
        self.conn = pymysql.connect(
            host='localhost',
            port=3306,
            user='root',
            passwd='root',
            db='scenic1',
            charset='utf8mb4'
        )
        self.weather_df=pd.read_csv('timeing/weather_data.csv')

    def is_holiday(self, date):
        if date in ['2024-09-03', '2024-10-01', '2024-10-02', '2024-10-03', '2024-10-04', '2024-10-05', '2024-10-06', '2024-10-07', '2025-01-01', '2025-01-02', '2025-01-03']:
            return 1
        return 0
        
    def get_data(self):
        cursor = self.conn.cursor(cursor=pymysql.cursors.DictCursor)
        # 获取入园数据
        sql = """
        SELECT DATE(g.create_time) as date, count(*) as count
        FROM order_user_gate_rel g WHERE g.create_time BETWEEN '2024-07-01' and '2025-01-01' GROUP BY date
        """
        cursor.execute(sql)
        ret = cursor.fetchall()
        df = pd.DataFrame(ret)
        # 合并天气数据
        self.weather_df = pd.read_csv('timeing/weather_data.csv')
        self.weather_df['date'] = pd.to_datetime(self.weather_df['date'])
        df['date'] = pd.to_datetime(df['date'])
        df_pivot = pd.merge(df, self.weather_df, on='date')
        df_pivot.set_index('date', inplace=True)
        df_pivot['dow'] = df_pivot.index.dayofweek  # 星期几(0-6)
        df_pivot['month'] = df_pivot.index.month  # 月份
        df_pivot['is_holiday'] = df_pivot.index.map(self.is_holiday)

        # 对星期几和月份、天气和风力、风向进行独热编码
        df_pivot = pd.get_dummies(df_pivot, columns=['dow', 'month', 'tianqi', 'fengli', 'fengxiang'], dtype=int)

        # 对温度进行类型转换
        df_pivot['bWendu'] = df_pivot['bWendu'].str.replace('°', '').astype(int)
        df_pivot['yWendu'] = df_pivot['yWendu'].str.replace('°', '').astype(int)

        # 归一化入园数
        scaler = MinMaxScaler()
        feature = df_pivot[['count']]
        df_pivot['count'] = scaler.fit_transform(feature)
        dump(scaler, 'timeing/scaler.joblib')

        # 归一化天气
        weather_features = ['bWendu', 'yWendu']
        df_pivot[weather_features] = scaler.fit_transform(df_pivot[weather_features])
        dump(scaler, 'timeing/weather_scaler.joblib')

        df_pivot.to_csv('timeing/scenic_data.csv', index=False)
        
        
if __name__ == '__main__':
    # wu=weatherUtils()
    # wu.get_data()
    mu = MySQLUtils()
    mu.get_data()